13 papers
Anomaly-Informed Confidence Calibration for Vision-Based Safety Prediction
Zhenjiang Mao, Jiawen Wu, Gabriel Wagner +2
Reliable confidence estimates are important for safely deploying vision-based controllers in autonomous racing, where safety predictions must be derived from camera images, yet mod…
TEACar: An Open-Source Autonomous Driving Platform
Zhongzheng Zhang, Maxwell Ruyle, Andrew Kappes +5
Intelligent Transportation Systems (ITS) increasingly rely on vision-based perception and learning-based control, necessitating experimental platforms that support realistic hardwa…
Statistical-Symbolic Verification of Perception-Based Autonomous Systems using State-Dependent Conformal Prediction
Yuang Geng, Thomas Waite, Trevor Turnquist +2
Reachability analysis has been a prominent way to provide safety guarantees for neurally controlled autonomous systems, but its direct application to neural perception components i…
Physically Interpretable World Models via Weakly Supervised Representation Learning
Zhenjiang Mao, Mrinall Eashaan Umasudhan, Ivan Ruchkin
Learning predictive models from high-dimensional sensory observations is fundamental for cyber-physical systems, yet the latent representations learned by standard world models lac…
Deterministic World Models for Closed-loop Reachability Analysis of End-to-End Vision-based Control
Yuang Geng, Zhuoyang Zhou, Zhongzheng Zhang +6
End-to-end image controllers that map raw camera frames directly to control actions are increasingly deployed in safety-critical systems. However, formally verifying their closed-l…
How Safe Will I Be Given What I Saw? Calibrated Prediction of Safety Chances for Image-Controlled Autonomy
Zhenjiang Mao, Mrinall Eashaan Umasudhan, Ivan Ruchkin
Autonomous robots that rely on deep neural network controllers pose critical challenges for safety prediction, especially under partial observability and distribution shift. Tradit…